The reward structure of science varies a lot across different fields and countries. It’s worth thinking about how different reward structures affect the behavior of scientists and thus the direction of science as a whole.
For instance, in the past I’ve discussed whether it’s suboptimal for funding agencies looking to maximize bang for the buck to give big grants to only a few people and nothing to most people. Conversely, Brian’s noted that, if you think of scientific productivity as reflecting your ability to jump a series of hurdles, then you expect a small fraction of scientists to be much more productive than the others. Arguably, those productive few should be rewarded accordingly. Or think of the possibility that the scarcity of tenure-track jobs, grants, and space in leading journals, relative to the demand for them, creates incentives for scientists to behave in ways that are bad for science as a whole (e.g., incentives to oversell or even falsify one’s results).
This new (unreviewed) preprint drove home to me just how much variation in reward structure there is among fields. It uses comparative analysis of citation patterns and other data to show that economics is much more hierarchically organized than other social science fields. By “hierarchically organized”, I mean that faculty and students associated with a relatively small number of top economics departments tend to publish a disproportionately large fraction of papers in top journals, tend to get hired as assistant professors, etc. In contrast, I bet if you did similar analyses of ecology, you’d find it less hierarchically organized than economics.* (Note that this preprint also covers other issues that aren’t relevant here.)
Your mileage may vary on whether you see strongly-hierarchical systems that give big rewards to small numbers of people as a good thing or a bad thing for a field as a whole. Economists Claudia Sahm and Paul Krugman have interesting comments on how the strong hierarchical organization of economics can be both good and bad for economics. For instance, Sahm suggests that a strongly hierarchical reward structure might reward people who have mastered the intellectual “status quo”. Which is a good thing insofar as the status quo is good. But it makes it hard for the field to cultivate alternative viewpoints as a hedge against the status quo being seriously flawed.
There’s at least a bit of modeling work on what sorts of reward structures promote the most rapid progress of science as a whole. For instance, Strevens (2003) argues that, under some seemingly-mild assumptions, a “winner take all” system in which all the rewards go to the first person to solve a scientific problem maximizes the probability that the problem will be solved. In part because it creates incentives for scientists collectively to diversify the range of approaches they take to solving the problem. See here (section 5.2) for a good discussion. Strevens (2003) argues that this explains why scientists care so much about priority, about rewarding the first person to discover something. Of course, Strevens (2003) considers a deliberately simplified and artificial situation, but I still found it interesting. It undermines the widespread intuition that “winner take all” reward structures incentivize intellectual conservatism over risk-taking. And I do think Strevens might be on to something here. For instance, his model resonates with David Hembry’s discussion of choosing between working in a system in which lots of other people are working, and the riskier but possibly more rewarding choice of working in a less popular system (further discussion here). It would be interesting to try to extend this sort of model to more realistic situations and see how the optimal reward structure changes (if it does), and how a “winner take all” reward system shapes the conduct of science. Indeed, Michael Strevens himself seems to be doing that, and maybe others are too (it’s not my field, so I have no idea).
Of course, the reward structure that’s best for science as a whole might be less than desirable from the perspective of some or even many individual scientists. Lots that could be said here, obviously. It’s my anecdotal impression that arguments about lots of aspects of how science works–not just the reward structure–come down to interlinked disagreements about what’s best for science as a whole, vs. what’s best for individual scientists. It’s also my anecdotal impression that what’s best for science as a whole, and what’s best for individual scientists, often gets conflated. That’s unfortunate; I think it’s important to keep those two things separate. For instance, I think that by far the strongest argument for an NSERC-type grant funding system with high success rates but a modest average grant size is the argument that it’s good for science as a whole, for various reasons. That it also makes individual PIs happy is a pleasant side effect, but not an argument for the system. It’s not NSERC’s job to make PIs happy, it’s NSERC’s job to buy as much good science as it can.
Some aspects of the reward structure of science would be much easier to change than others, if we wanted to. For instance, a granting agency that currently gives out only a few big grants can just decide to give out more, smaller grants. In contrast, the distribution of attention paid to pretty much anything people pay attention to is very highly skewed, with a small fraction of stuff garnering a large fraction of the audience’s collective attention. That’s as true for scientific papers and citations as anything else, so it’s hard to see how you could change that even if you wanted to.
I don’t have any answers here. It’s not obvious to me that there’s any single “best” reward structure for all fields in all circumstances. For instance, if you look at various measures of “bang for the buck”, you find that countries with quite differently-structured funding systems all are scientifically productive. Rather, my intuition is that there are trade-offs here, so that different reward structures each have their own strengths and weaknesses, and that particular strengths and particular weaknesses tend to go hand in hand. Further, it’s certainly possible that all existing reward structures fall short of what would be optimal in some hypothetical “ideal” world.
Looking forward to comments, particularly anyone who can link to further data and modeling on this.
*There are some similar analyses of other fields. For instance, in biomedical fields in the US, the majority of new assistant professors did a postdoc with a member of the National Academy of Sciences.